Collecting High-Quality Data

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
Social Desirability ◽  
Measurement Errors ◽  
Monitoring And Evaluation ◽  
Quality Data ◽  
Social Desirability Bias ◽  
High Quality ◽  
High Quality Data ◽  
Evaluation Systems ◽  
Demand Effect

Without high-quality data, even the best-designed monitoring and evaluation systems will collapse. Chapter 7 introduces some the basics of collecting high-quality data and discusses how to address challenges that frequently arise. High-quality data must be clearly defined and have an indicator that validly and reliably measures the intended concept. The chapter then explains how to avoid common biases and measurement errors like anchoring, social desirability bias, the experimenter demand effect, unclear wording, long recall periods, and translation context. It then guides organizations on how to find indicators, test data collection instruments, manage surveys, and train staff appropriately for data collection and entry.

scholarly journals Information Explanatory Applications in Street Activity Security – A Survey

2020 ◽  
pp. 81-83
Author(s):  
Samsudeen S ◽  
Salomi M
Keyword(s):  
Data Collection ◽  
Path Selection ◽  
Data Sources ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Start Up ◽  
Improvement Strategies ◽  
Paper Survey

The paper survey helps to diminish the start-up complex of knowledge assortment and clear analytics for factual modeling & course improvement for probability connected by engine vehicles. We tend to seem that the writing is isolated into 2 totally different inquire concerning areas: (a) discerning/illustrative methods which endeavor in order to urge it and assess clatter hazard supported distinctive powerful conditions, and (b) improvement strategies which center by minimizing clatter probability by route, path-selection and break design. Interpretation based on inquire concerning results of the 2 streams are restricted to beat the problem that tends to show freely accessible high-quality data sources (diverse take into account plans, result factors, and indicator factors) and communicative instructive strategies (information summarization, visualization, and measuring decrease) which are used for understanding safer-routing and provides code to encourage data collection/exploration by practitioners/res

scholarly journals Data Fusion for Correcting Measurement Errors

2018 ◽  
Vol 7 (2) ◽  
pp. 175-200
Author(s):  
Tracy Schifeling ◽  
Jerome P Reiter ◽  
Maria Deyoreo
Keyword(s):  
Data Fusion ◽  
National Survey ◽  
Measurement Errors ◽  
College Graduates ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Unrealistic Assumption ◽  
Data Source ◽  
Fusion Framework

AbstractOften in surveys, key items are subject to measurement errors. Given just the data, it can be difficult to determine the extent and distribution of this error process and, hence, to obtain accurate inferences that involve the error-prone variables. In some settings, however, analysts have access to a data source on different individuals with high-quality measurements of the error-prone survey items. We present a data fusion framework for leveraging this information to improve inferences in the error-prone survey. The basic idea is to posit models about the rates at which individuals make errors, coupled with models for the values reported when errors are made. This can avoid the unrealistic assumption of conditional independence typically used in data fusion. We apply the approach on the reported values of educational attainments in the American Community Survey, using the National Survey of College Graduates as the high-quality data source. In doing so, we account for the sampling design used to select the National Survey of College Graduates. We also present a process for assessing the sensitivity of various analyses to different choices for the measurement error models. Supplemental material is available online.

A fibre-based crystal mounting technique for protein cryocrystallography

2012 ◽  
Vol 45 (2) ◽  
pp. 362-366 ◽  
Author(s):  
Michihiro Sugahara
Keyword(s):  
Data Collection ◽  
Success Rate ◽  
Single Crystals ◽  
Conventional Technique ◽  
Synthetic Zeolite ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
X Ray ◽  
Loop Technique

The CryoFibre, a crystal mounting tool, has been developed for protein cryocrystallography. The technique attaches single crystals to the tips of polyester fibres, allowing removal of excess liquid around each crystal. Single-wavelength anomalous dispersion phasing using a Cu Kα X-ray source (Cu SAD) was applied to crystals from five proteins without any derivatization, demonstrating a clear improvement in the success rate of Cu SAD compared with the conventional loop technique. In addition, a xylanase crystal on the surface of a synthetic zeolite as a hetero-epitaxic nucleant was directly mounted on the CryoFibre without separation treatment of the crystal from the zeolite. The crystal had a lower mosaicity than that observed using the conventional technique, indicating that the fibre technique is suitable for high-quality data collection from zeolite-mediated crystals.

The CART Principles in More Detail

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
Theory Of Change ◽  
Quality Data ◽  
Opportunity Costs ◽  
High Quality ◽  
High Quality Data

Chapter 4 discusses the CART principles in more detail, showing how they can help organizations make difficult tradeoffs about the data they should collect. To ensure credibility, organizations should collect high-quality data and analyze them accurately. This means that all data collected must be valid, reliable, and appropriately used. Actionability requires that organizations only collect data they can commit to use. This chapter explains how the actionable principle, combined with a well-articulated theory of change, guides organizations to only collect data that will have a specific use. It then explains that, for credible data collection, organizations must ensure that the benefits of data collection outweigh the costs. All data have opportunity costs—the money and time spent collecting data could also be spent implementing programs. Finally, it explains how organizations can collect transportable data that can generate knowledge for other programs.

scholarly journals Low-cost double pendulum for high-quality data collection with open-source video tracking and analysis

HardwareX ◽  
2020 ◽  
Vol 8 ◽  
pp. e00138
Author(s):  
Audun D. Myers ◽  
Joshua R. Tempelman ◽  
David Petrushenko ◽  
Firas A. Khasawneh
Keyword(s):  
Data Collection ◽  
Open Source ◽  
Low Cost ◽  
Video Tracking ◽  
Quality Data ◽  
Double Pendulum ◽  
High Quality ◽  
High Quality Data
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